Abstract 9295: Ertugliflozin Has Favorable Effects on Kidney Outcomes in Patients With Baseline Evidence of Heart Failure in VERTIS CV
Bibliographic record
Abstract
Background: Data from VERTIS CV (NCT01986881) were analyzed to study the effect of ertugliflozin on kidney outcomes in patients with and without baseline evidence of HF. Methods: Patients with type 2 diabetes mellitus and ASCVD were randomized (1:1:1) to ertugliflozin 5 or 15 mg (both doses pooled for analyses) or placebo. Subgroups were defined by baseline history of HF or a pre-randomization left ventricular ejection fraction (EF) ≤45%. eGFR over time, total 5-year eGFR slopes and Cox proportional hazards for time to first event of a kidney composite of sustained ≥40% decrease from baseline eGFR, chronic kidney replacement therapy, or kidney death were assessed. Results: At baseline, 2439 (29.6%) had a history of HF or EF ≤45% (HF) and 5807 (70.4%) had no evidence of HF (no HF). Compared with placebo, ertugliflozin treatment resulted in a slower rate of eGFR decline in both subgroups; total placebo-adjusted 5-year eGFR slope (mL/min/1.73 m 2 /year [95% CI]): 0.96 (0.67, 1.24) and 0.95 (0.76, 1,14) for the HF and no HF subgroups, respectively (Figure 1). The HF subgroup had a higher event rate for the composite kidney outcome than the no HF subgroup. Hazard ratios (95% CI) for ertugliflozin for reducing the risk of the composite kidney outcome did not differ significantly across the two subgroups; 0.53 (0.33, 0.84) and 0.76 (0.53, 1.08) for the HF and no HF subgroups, respectively ( P interaction =0.22) (Figure 2). Conclusion: In VERTIS CV, patients with HF at baseline had a notably faster rate of eGFR decline that is unlikely to be explained by the slightly lower baseline eGFR in that group. The effects of ertugliflozin on kidney outcomes did not differ between the two groups stratified by baseline HF.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".